Skip to content

fix: evaluate sort-merge join filters that reference no columns #26010

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
mrhard9090 wants to merge 1 commit into
base: main
Choose a base branch
from
+101 −59
Open

fix: evaluate sort-merge join filters that reference no columns #26010

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
122 changes: 63 additions & 59 deletions datafusion/physical-plan/src/joins/sort_merge_join/materializing_stream.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -46,6 +46,7 @@ use arrow::compute::{
take_arrays,
};
use arrow::datatypes::SchemaRef;
use arrow::record_batch::RecordBatchOptions;
use datafusion_common::cast::as_uint64_array;
use datafusion_common::instant::Instant;
use datafusion_common::{
Expand Down Expand Up @@ -1599,72 +1600,75 @@ impl MaterializingSortMergeJoinStream {
};
let output_batch = RecordBatch::try_new(Arc::clone(&self.schema), columns)?;

if !filter_columns.is_empty() {
if let Some(f) = &self.filter {
let filter_batch =
RecordBatch::try_new(Arc::clone(f.schema()), filter_columns)?;
let filter_result = f
.expression()
.evaluate(&filter_batch)?
.into_array(filter_batch.num_rows())?;

let filter_result_mask =
datafusion_common::cast::as_boolean_array(&filter_result)?;

// Convert NULL filter results to false — NULL means "not satisfied"
// per SQL semantics, same as Left/Right outer joins.
let mask = if filter_result_mask.null_count() > 0 {
compute::prep_null_mask_filter(filter_result_mask)
} else {
filter_result_mask.clone()
};
// A filter that reads no columns (e.g. a bound parameter or a volatile
// function) still has to be evaluated, so key on the filter itself.
if let Some(f) = &self.filter {
let filter_batch = RecordBatch::try_new_with_options(
Arc::clone(f.schema()),
filter_columns,
&RecordBatchOptions::new().with_row_count(Some(total_matched_rows)),
)?;
let filter_result = f
.expression()
.evaluate(&filter_batch)?
.into_array(filter_batch.num_rows())?;

let filter_result_mask =
datafusion_common::cast::as_boolean_array(&filter_result)?;

// Convert NULL filter results to false — NULL means "not satisfied"
// per SQL semantics, same as Left/Right outer joins.
let mask = if filter_result_mask.null_count() > 0 {
compute::prep_null_mask_filter(filter_result_mask)
} else {
filter_result_mask.clone()
};

if self.deferred_filtering {
self.joined_record_batches.push_batch_with_filter_metadata(
output_batch,
&combined_left_indices,
&mask,
self.streamed_batch_counter,
self.join_type,
);
} else {
let filtered_batch = filter_record_batch(&output_batch, &mask)?;
self.joined_record_batches
.push_batch_without_metadata(filtered_batch);
}
if self.deferred_filtering {
self.joined_record_batches.push_batch_with_filter_metadata(
output_batch,
&combined_left_indices,
&mask,
self.streamed_batch_counter,
self.join_type,
);
} else {
let filtered_batch = filter_record_batch(&output_batch, &mask)?;
self.joined_record_batches
.push_batch_without_metadata(filtered_batch);
}

// Track which buffered rows had all filter matches fail,
// so full join can emit them as null-joined later.
if self.join_type == JoinType::Full {
let mut offset = 0usize;
for (batch_idx, _left, right) in matched_chunks {
let chunk_len = right.len();
let buffered_batch = &mut self.buffered_data.batches[*batch_idx];

for i in 0..chunk_len {
if right.is_null(i) {
continue;
// Track which buffered rows had all filter matches fail,
// so full join can emit them as null-joined later.
if self.join_type == JoinType::Full {
let mut offset = 0usize;
for (batch_idx, _left, right) in matched_chunks {
let chunk_len = right.len();
let buffered_batch = &mut self.buffered_data.batches[*batch_idx];

for i in 0..chunk_len {
if right.is_null(i) {
continue;
}
let idx = right.value(i) as usize;
match buffered_batch.join_filter_status[idx] {
FilterState::SomePassed => {}
_ if mask.value(offset + i) => {
buffered_batch.join_filter_status[idx] =
FilterState::SomePassed;
}
let idx = right.value(i) as usize;
match buffered_batch.join_filter_status[idx] {
FilterState::SomePassed => {}
_ if mask.value(offset + i) => {
buffered_batch.join_filter_status[idx] =
FilterState::SomePassed;
}
_ => {
buffered_batch.join_filter_status[idx] =
FilterState::AllFailed;
}
_ => {
buffered_batch.join_filter_status[idx] =
FilterState::AllFailed;
}
}
offset += chunk_len;
}
debug_assert_eq!(
offset, total_matched_rows,
"offset must advance through every chunk exactly once"
);
offset += chunk_len;
}
debug_assert_eq!(
offset, total_matched_rows,
"offset must advance through every chunk exactly once"
);
}
} else {
self.joined_record_batches
Expand Down
38 changes: 38 additions & 0 deletions datafusion/sqllogictest/test_files/sort_merge_join.slt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -971,6 +971,44 @@ WHERE t1_sorted.data < 0
----
100

# Join filter that reads no columns: it must still be evaluated for every
# candidate pair (hash join already does), so FULL JOIN null-pads both sides.
statement ok
CREATE TABLE smj_nocol_a AS SELECT * FROM (VALUES (1, 10), (2, 20), (3, 30)) t(k, v);

statement ok
CREATE TABLE smj_nocol_b AS SELECT * FROM (VALUES (1, 100), (2, 200), (4, 400)) t(k, w);

statement ok
set datafusion.execution.target_partitions = 2;

query IIII
SELECT * FROM smj_nocol_a a FULL JOIN smj_nocol_b b ON a.k = b.k AND random() > 2 ORDER BY a.k, b.k;
----
1 10 NULL NULL
2 20 NULL NULL
3 30 NULL NULL
NULL NULL 1 100
NULL NULL 2 200
NULL NULL 4 400

query IIII
SELECT * FROM smj_nocol_a a FULL JOIN smj_nocol_b b ON a.k = b.k AND random() < 2 ORDER BY a.k, b.k;
----
1 10 1 100
2 20 2 200
3 30 NULL NULL
NULL NULL 4 400

statement ok
set datafusion.execution.target_partitions = 4;

statement ok
DROP TABLE smj_nocol_a;

statement ok
DROP TABLE smj_nocol_b;

# Config reset
statement ok
RESET datafusion.optimizer.prefer_hash_join;
Loading